A prognosis prediction chromatin regulator signature for patients with severe asthma
Bibliographic record
Abstract
Severe asthma imposes a physical and economic burden on both patients and society. As chromatin regulators (CRs) influence the progression of multiple diseases through epigenetic mechanisms, we aimed to study the role of CRs in patients with severe asthma. Transcriptome data (GSE143303) from 47 patients with severe asthma and 13 healthy participants was downloaded from the Gene Expression Omnibus database. Enrichment analysis was performed to investigate the functions of differentially expressed CRs between the groups. We identified 80 differentially expressed CRs; they were mainly enriched in histone modification, chromatin organization, and lysine degradation. A protein-protein interaction network was then constructed. The analyzed immune scores were different between sick and healthy individuals. Thus, CRs with a high correlation in the immune analysis, SMARCC1, SETD2, KMT2B, and CHD8, were used to construct a nomogram model. Finally, using online prediction tools, we determined that lanatoside C, cefepime, and methapyrilene may be potentially effective drugs in the treatment of severe asthma. The nomogram constructed using the four CRs, SMARCC1, SETD2, KMT2B, and CHD8, may be a useful tool for predicting the prognosis of patients with severe asthma. This study provided new insights into the role of CRs in severe asthma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".